Gary Ackerman @garyackerman@qoto.org

The AI control problem is paramount: ensuring future advanced AI remains beneficial and aligned with human values, avoiding potential existential risks. #AIControl #ExistentialRisk

In accident scenarios, potential AI ethical programming includes Retributivist (harm responsible party), Selfish (protect AI's occupant), and Utilitarian (minimize overall harm). Each has implications. #AIethics #DecisionMaking

The concept of AI personhood is highly debated. Should AI systems have rights or responsibilities similar to humans or corporations?

The propagation of misinformation online, sometimes amplified by AI, is a serious issue impacting politics and societal trust. #Disinformation #AI

Companies leverage user-generated data to improve services and user experience, but this practice has led to concerns and regulations like GDPR. #DataPrivacy #UserData

As automation progresses, workers will likely need to acquire new skills to remain competitive. This shift could widen the gap in income inequality. #Upskilling #Workforce

Beyond just job displacement, the increasing capabilities of AI, especially in mimicking human intelligence, raise complex ethical questions.

AI has the potential to increase productivity, but its impact on economic inequality is a significant concern.

Asimov's Three Laws of Robotics outline foundational rules for robots: no harm to humans, obey human orders (unless conflicting with rule 1), and protect self (unless conflicting with rules 1 or 2).

Learning Analytics can operate at four levels: Descriptive (what happened?), Diagnostic (why?), Predictive (what will happen?), and Prescriptive (what action to take?). Each level builds upon the last.

#AI is reshaping education and the fundamental relationship between technology & humans. It's considered essential for the education ecosystem, preparing future generations. #EdTech #ArtificialIntelligence

Empirical support for ICAP shows that I>C>A>P learning effectiveness is consistent with pair-wise predictions. Many studies support the idea that Interactive, Constructive, & Active are better than Passive.

Extraneous Cognitive Load is imposed by instructional procedures that unnecessarily increase element interactivity. Good instruction aims to reduce this.

Biologically secondary knowledge, like reading or most school subjects, requires explicit instruction and conscious learning because we haven't evolved to acquire it automatically.

Biologically primary knowledge, like learning to speak, is acquired automatically, without explicit tuition or conscious effort. #PrimaryKnowledge #AutomaticLearning

Knowledge can be split into two categories: biologically primary (evolved to acquire, generic) and biologically secondary (cultural, domain-specific).

Variables that affect how innovations "happen" in schools: improvement (solving the problem), ease of use, perceived value (addressing the organizational understanding of the problem), and demonstrability (effectiveness perceived by others).

School leaders find technology planning to be an area of weakness despite the prevalence of digital tools in schools. They struggle to make decisions that are appropriate for the classroom, aligned with policies, and supported by available resources.

Contrary to intuition, forgetting is not the enemy of learning. In the adaptive system of human learning and memory, forgetting can actually provide opportunities for enhanced learning.

Making things "Hard on Yourself, But in a Good Way": Desirable difficulties involve implementing learning strategies such as varying the conditions of study, interleaving different topics, spacing out study sessions (instead of massing them), and using tests or retrieval practice as learning events. #learning